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Inference of three-dimensional hot-spot and shell morphology in inertial confinement fusion experiments using a convolutional neural network

K. M. Woo1,*, J. Buck2, K. Churnetski1, R. Betti1, C. A. Thomas1, C. Stoeckl1, P. V. Heuer1, C. Kanan3, P. B. Radha1 et al.

J. Carroll-Nellenback1, K. S. Anderson1, T. J. B. Collins1, L. Ceurvorst1, V. Gopalaswamy1, and S. T. Ivancic1

  • 1Laboratory for Laser Energetics, Rochester, New York 14623, USA
  • 2Department of Physics and Astronomy, University of Rochester, Rochester, New York 14623, USA
  • 3Department of Computer Science, University of Rochester, Rochester, New York 14623, USA

  • *Contact author: kwoo@lle.rochester.edu
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Phys. Rev. Research 7, 023272 – Published 17 June, 2025

DOI: https://doi.org/10.1103/krtw-bkc3

Abstract

The performance of inertial confinement fusion (ICF) implosions is sensitive to the three-dimensional (3D) morphology of the hot-spot and shell configurations. The ability to infer shell-mass uniformity and reconstruct 3D hot spots is crucial for quantifying the degradation of ignition criteria and improving symmetry in ICF implosion experiments. In this work, we present a deep-learning convolutional neural network (CNN) for reconstructing 3D hot-spot and shell structures for ICF capsules. The 3D geometry of the hot spot is reconstructed from x-ray images measured from multiple lines of sight on OMEGA. The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. This work demonstrates a CNN framework that successfully reconstructs 3D capsule structures from two-dimensional images in ICF implosions.

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Phys. Rev. Research 7, 023272– Published 17 June, 2025
  • Received 11 March 2024
  • Accepted 9 May 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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